“The Socioeconomic High-resolution Rural-Urban Geographic Platform for India (SHRUG) is a geographic platform that facilitates data sharing between researchers working on India. It is an open access repository currently comprising dozens of datasets covering India’s 500,000 villages and 8000 towns using a set of a common geographic identifiers that span 25 years….”
“ODISSEI (Open Data Infrastructure for Social Science and Economic Innovations) is the national research infrastructure for the social sciences in the Netherlands. ODISSEI brings together researchers with the necessary data, expertise and resources to conduct ground-breaking research and embrace the computational turn in social enquiry….”
“In these uncertain times, some faculty and their students may not have access to their institution’s print-only subscription to the AEA journals. To ensure access as academic semesters and various student projects conclude, the Association is making its available journal content open access on the AEA website through June 30, 2020. We thank you for your support as a member of the Association and hope that you will share this announcement with your colleagues and students who may not currently have online access through their institutional libraries. Please visit www.aeaweb.org to access the journals.”
“In Steffen’s view, new open access payment models are needed to make open access implementation practical. The journal he co-edits, EER Plus, was launched in 2019 as the OA spin-off of Europe’s oldest general-interest economics journals: European Economic Review (EER). Its quality and reputation are such that it rejects about 80 percent of papers.
As Steffen describes it, the EPC model his journal is piloting offers an affordable option for researchers with limited access to funds. The charge is set low – at €527, where some article processing charges will be upwards of €4,000 – and unlike a submission fee, the author only pays if their paper is selected for peer review. However, that fee is non-refundable if the article is rejected at the peer review stage….”
Abstract: This study estimates the effect of data sharing on the citations of academic articles, using journal policies as a natural experiment. We begin by examining 17 high-impact journals that have adopted the requirement that data from published articles be publicly posted. We match these 17 journals to 13 journals without policy changes and find that empirical articles published just before their change in editorial policy have citation rates with no statistically significant difference from those published shortly after the shift. We then ask whether this null result stems from poor compliance with data sharing policies, and use the data sharing policy changes as instrumental variables to examine more closely two leading journals in economics and political science with relatively strong enforcement of new data policies. We find that articles that make their data available receive 97 additional citations (estimate standard error of 34). We conclude that: a) authors who share data may be rewarded eventually with additional scholarly citations, and b) data-posting policies alone do not increase the impact of articles published in a journal unless those policies are enforced.
Abstract: The goal of this research is to examine and explore information retrieval process of patrons who access institutional repositories. Repositories are generally hosted by public universities and run by volunteers which allow researchers to submit their draft versions of their manuscripts in pre-print forms. In this study, we analyze using search methods to sort out research papers classified according to their levels of relevance that are available from a repository, and report the pattern of search results as our findings. Our model employs search methods for searching Econpapers which utilize RePEc bibliographic data. Our analysis attempts to highlight how information seekers, scholars and researchers search relevant topics of their interest and how relevant such information is which is retrieved from an institutional repository. This could aid researchers to modify their search processes to obtain better search results from their queries. The goal is to obtain the most relevant documents from online search. We discuss about the methods employed to retrieve information which is most pertinent to the requirements of researchers. A broad implication could be better utilization of time and resources for efficient retrieval of the most relevant documents of interest that could be expected from searching institutional repositories.
Abstract: In the last 3 years, several new (free) sources for academic publication and citation data have joined the now well-established Google Scholar, complementing the two traditional commercial data sources: Scopus and the Web of Science. The most important of these new data sources are Microsoft Academic (2016), Crossref (2017) and Dimensions (2018). Whereas Microsoft Academic has received some attention from the bibliometric commu-nity, there are as yet very few studies that have investigated the coverage of Crossref or Dimensions. To address this gap, this brief letter assesses Crossref and Dimensions cover-age in comparison to Google Scholar, Microsoft Academic, Scopus and the Web of Science through a detailed investigation of the full publication and citation record of a single academic, as well as six top journals in Business & Economics. Overall, this first small-scale study suggests that, when compared to Scopus and the Web of Science, Crossref and Dimensions have a similar or better coverage for both publications and citations, but a substantively lower coverage than Google Scholar and Microsoft Academic. If our find-ings can be confirmed by larger-scale studies, Crossref and Dimensions might serve as good alternatives to Scopus and the Web of Science for both literature reviews and citation analysis. However, Google Scholar and Microsoft Academic maintain their position as the most comprehensive free sources for publication and citation data
Abstract: Many studies have explored the relationship between housing prices and environmental characteristics using the hedonic price model (HPM). However, few studies have deeply examined the impact of scene perception near residential units on housing prices. This article used house purchasing records from FANG.com and open access geolocation data (including massive street view pictures, point of interest (POI) data and road network data) and proposed a framework named “open-access-dataset-based hedonic price modeling (OADB-HPM)” for comprehensive analysis in Beijing and Shanghai, China. A state-of-the-art deep learning framework and massive Baidu street view panoramas were employed to visualize and quantify three major scene perception characteristics (greenery, sky and building view indexes, abbreviated GVI, SVI and BVI, respectively) at the street level. Then, the newly introduced scene perception characteristics were combined with other traditional characteristics in the HPM to calculate marginal prices, and the results for Beijing and Shanghai were explored and compared. The empirical results showed that the greenery and sky perceptual elements at the property level can significantly increase the housing price in Beijing (RMB 39,377 and 6011, respectively) and Shanghai (RMB 21,689 and 2763, respectively), indicating an objectively higher willingness by buyers to pay for houses that provide the ability to perceive natural elements in the surrounding environment. This study developed quantification tools to help decision makers and planners understand and analyze the interaction between residents and urban scene components.
“Romer believes in making research transparent. He argues that openness and clarity about methodology is important for scientific research to gain trust. As Romer explained in an April 2018 blog post, in an effort to make his own work transparent, he tried to use Mathematica to share one of his studies in a way that anyone could explore every detail of his data and methods. It didn’t work. He says that Mathematica’s owner, Wolfram Research, made it too difficult to share his work in a way that didn’t require other people to use the proprietary software, too. Readers also could not see all of the code he used for his equations.
Instead of using Mathematica, Romer discovered that he could use a Jupyter notebook for sharing his research. Jupyter notebooks are web applications that allow programmers and researchers to share documents that include code, charts, equations, and data. Jupyter notebooks allow for code written in dozens of programming languages. For his research, Romer used Python—the most popular language for data science and statistics.
Importantly, unlike notebooks made from Mathematica, Jupyter notebooks are open source, which means that anyone can look at all of the code that created them. …”